Morphomics Report

devtools::load_all("D:/source/SpatialPathomicsToolkit")

library(knitr)
library(tidyverse)

Load Data

dataDir="d:\\workSync\\HaichunYang\\202306_podocyteMorphomics\\202310_data\\"
setwd(dataDir)

files=list.files(pattern="*.csv")
samples=gsub("_.*","",files)
samples=gsub("6793-","",samples)
metaTable=data.frame(Sample=samples,File=files,stringsAsFactors=FALSE)

#celltypes=sapply(strsplit(files,"_"),function(x) x[2])
#metaTable=data.frame(Sample=samples,File=files,CellType=celltypes,stringsAsFactors=FALSE)

featureDataAll=ReadMorphomicFeatures(metaTable)

#add celltype column
featureDataAll[["SampleData"]]$CellType=sapply(strsplit(featureDataAll[["SampleData"]]$FileName,"_"),function(x) x[1])

Data Transformation

#temp=DataSelection(featureDataAll,featureType=c("AreaShape","Intensity"))
featureDataAllNormlized=MorphomicFeaturesNormlization(featureDataAll)

## [1] "Remove variables less than 10 unique values"
## [1] "23 features removed"
## [1] "Number of features after selection:"
## 
##          AreaShape        Granularity          Intensity          Neighbors RadialDistribution            Texture 
##                104                  3                 15                  6                 67                 52 
## [1] "Remove variables with NA"
## [1] "3 features removed"
## [1] "AngleBetweenNeighbors_Expanded;FirstClosestDistance_Expanded;SecondClosestDistance_Expanded"
## [1] "Number of features after selection:"
## 
##          AreaShape        Granularity          Intensity          Neighbors RadialDistribution            Texture 
##                104                  3                 15                  3                 67                 52 
## [1] "Remove variables less than 2% subjects with unique values"
## [1] "Normalization done"

Data Describe

library(Hmisc)

## Warning: package 'Hmisc' was built under R version 4.2.3

Raw

varForTable1=colnames(featureDataAllNormlized[["FeatureData"]])
s=describe(featureDataAllNormlized[["FeatureData"]][,varForTable1])
html(s, exclude1=FALSE,  what=c('%'),digits=3, prmsd=TRUE)

Transformation summary

print(table(featureDataAllNormlized[["FeatureDataNormlizedSummary"]]$note))

## 
##                          BoxCox (1)                    Boxcox trans.(2)                          Log trans.                            Raw data Raw due to fail in Boxcox trans.(2) 
##                                  13                                  16                                  92                                 121                                   2

After data Transformation

varForTable1=colnames(featureDataAllNormlized[["FeatureDataNormlized"]])
s=describe(featureDataAllNormlized[["FeatureDataNormlized"]][,varForTable1])
html(s, exclude1=FALSE,  what=c('%'),digits=3, prmsd=TRUE)

Perform PCA and Correlation

featureDataAllNormlized=featureDataPcaAll(featureDataAllNormlized)

PCA: Propotion of Variance Explained

By Feature Type

pList=list()
for (reductionName in c("AreaShape","Granularity"  ,"Intensity","Neighbors", "RadialDistribution" ,"Texture"  )){
  pList[[reductionName]]=plotPcaBarplot(featureDataAllNormlized,reductionName=paste0(reductionName,"_PCA"))+ggtitle(reductionName)
}

## Warning: package 'viridis' was built under R version 4.2.3

## Warning: package 'viridisLite' was built under R version 4.2.3

#show plots in pList by patchwork package
patchwork::wrap_plots(pList,ncol=2)

#patchwork::wrap_plots(pList,ncol=2,guides='collect')&scale_fill_gradientn(colors = color_palette,limits=c(0,30))

By Cell Type

pList=list()
for (reductionName in c("mesangial","PEC", "podocyte" ,"ALL"  )){
  pList[[reductionName]]=plotPcaBarplot(featureDataAllNormlized,reductionName=paste0(reductionName,"_PCA"))+ggtitle(reductionName)
}
patchwork::wrap_plots(pList,ncol=2)

PCA: Correlation of PCs in different cell

By Feature Type

# names(featureDataAllNormlized[["Reductions"]])
# 
# reductionName="Texture"
# temp1=featureDataAllNormlized[["Reductions"]][[reductionName]]$cell.embeddings[,3]
# 
# reductionName="podocyte_Texture"
# temp2=featureDataAllNormlized[["Reductions"]][[reductionName]]$cell.embeddings[,3]

plotPCsByCellType(featureDataAllNormlized,reductionName="AreaShape_PCA")

plotPCsByCellType(featureDataAllNormlized,reductionName="Granularity_PCA")

plotPCsByCellType(featureDataAllNormlized,reductionName="Intensity_PCA")

plotPCsByCellType(featureDataAllNormlized,reductionName="Neighbors_PCA")

plotPCsByCellType(featureDataAllNormlized,reductionName="RadialDistribution_PCA")

plotPCsByCellType(featureDataAllNormlized,reductionName="Texture_PCA")

All Features together

plotPCsByCellType(featureDataAllNormlized,reductionName="ALL_PCA",reductionNameToComp=c("mesangial_PCA","PEC_PCA" ,"podocyte_PCA"),PCs=1:9)

# plotPCsByCellType(featureDataAllNormlized,reductionName="ALL",reductionNameToComp=c("AreaShape","Granularity" ,"Intensity"),PCs=1:4)

PCA: Loading of PCs in different cell

plotPCsLoadingByCellType(featureDataAllNormlized,reductionName="AreaShape_PCA")

plotPCsLoadingByCellType(featureDataAllNormlized,reductionName="Granularity_PCA")

plotPCsLoadingByCellType(featureDataAllNormlized,reductionName="Intensity_PCA")

plotPCsLoadingByCellType(featureDataAllNormlized,reductionName="Neighbors_PCA")

plotPCsLoadingByCellType(featureDataAllNormlized,reductionName="RadialDistribution_PCA")

plotPCsLoadingByCellType(featureDataAllNormlized,reductionName="Texture_PCA")

All Features together

plotPCsLoadingByCellType(featureDataAllNormlized,reductionName="ALL_PCA",reductionNameToComp=c("mesangial_PCA","PEC_PCA" ,"podocyte_PCA"),PCs=1:9)

Feature Correlation Heatmap

plotCorHeatmap(featureDataAllNormlized,PCs=1:4)

plotCorHeatmap(featureDataAllNormlized,reductionName="podocyte_PCA",PCs=1:4)

plotCorHeatmap(featureDataAllNormlized,reductionName="Intensity_PCA",PCs=1:4)

plotCorHeatmap(featureDataAllNormlized,reductionName="podocyte_Intensity_PCA",PCs=1:4)

sparsePCA analysis

Perform sparsePCA based on PCA results

#another package
#devtools::install_github("erichson/spca")
library(sparsepca)

## Warning: package 'sparsepca' was built under R version 4.2.3

temp=featureDataAllNormlized
resultSparsePCA=featureDataSparsePca(temp,dataName="FeatureDataNormlized",k=30,alpha = 1e-03)
featureDataAllNormlized[["Reductions"]]$ALL_SparsePCA=resultSparsePCA


featureType="AreaShape"
temp=DataSelection(featureDataAllNormlized,featureType=featureType)

## [1] "AreaShape were selected as featureType"
## [1] "Number of features after selection:"
## 
## AreaShape 
##       104

resultSparsePCA=featureDataSparsePca(temp,dataName="FeatureDataNormlized",k=10,alpha = 1e-03)
featureDataAllNormlized[["Reductions"]]$AreaShape_SparsePCA=resultSparsePCA

featureType="Intensity"
temp=DataSelection(featureDataAllNormlized,featureType=featureType)

## [1] "Intensity were selected as featureType"
## [1] "Number of features after selection:"
## 
## Intensity 
##        15

resultSparsePCA=featureDataSparsePca(temp,dataName="FeatureDataNormlized",k=4,alpha = 1e-03)
featureDataAllNormlized[["Reductions"]]$Intensity_SparsePCA=resultSparsePCA

featureType="Neighbors"
temp=DataSelection(featureDataAllNormlized,featureType=featureType)

## [1] "Neighbors were selected as featureType"
## [1] "Number of features after selection:"
## 
## Neighbors 
##         3

resultSparsePCA=featureDataSparsePca(temp,dataName="FeatureDataNormlized",k=4,alpha = 1e-03)
featureDataAllNormlized[["Reductions"]]$Neighbors_SparsePCA=resultSparsePCA

featureType="RadialDistribution"
temp=DataSelection(featureDataAllNormlized,featureType=featureType)

## [1] "RadialDistribution were selected as featureType"
## [1] "Number of features after selection:"
## 
## RadialDistribution 
##                 67

resultSparsePCA=featureDataSparsePca(temp,dataName="FeatureDataNormlized",k=10,alpha = 1e-03)
featureDataAllNormlized[["Reductions"]]$RadialDistribution_SparsePCA=resultSparsePCA

featureType="Texture"
temp=DataSelection(featureDataAllNormlized,featureType=featureType)

## [1] "Texture were selected as featureType"
## [1] "Number of features after selection:"
## 
## Texture 
##      52

resultSparsePCA=featureDataSparsePca(temp,dataName="FeatureDataNormlized",k=4,alpha = 1e-03)
featureDataAllNormlized[["Reductions"]]$Texture_SparsePCA=resultSparsePCA

featureType="Granularity"
temp=DataSelection(featureDataAllNormlized,featureType=featureType)

## [1] "Granularity were selected as featureType"
## [1] "Number of features after selection:"
## 
## Granularity 
##           3

resultSparsePCA=featureDataSparsePca(temp,dataName="FeatureDataNormlized",k=4,alpha = 1e-03)
featureDataAllNormlized[["Reductions"]]$Granularity_SparsePCA=resultSparsePCA


# 
# dataForsPCA=temp[["FeatureDataNormlized"]]
# set.seed(123)
# rspca.results <- rspca((dataForsPCA), k=10, verbose=TRUE, max_iter=1000,center=TRUE, scale=TRUE)
# row.names(rspca.results$loadings)=colnames(dataForsPCA)
# head(rspca.results$loadings)
# 
# summary(rspca.results)
# 
# 
# featureType="Intensity"
# temp=DataSelection(featureDataAllNormlized,featureType=featureType)
# dataForsPCA=temp[["FeatureDataNormlized"]]
# 
# rspca.results <- rspca((dataForsPCA), k=5, verbose=TRUE, max_iter=1000,center=TRUE, scale=TRUE,alpha = 1e-03)
# row.names(rspca.results$loadings)=colnames(dataForsPCA)
# colnames(rspca.results$loadings)=paste0("PC",1:ncol(rspca.results$loadings))
# 
# head(rspca.results$loadings)
# #how many features for each PC
# apply(rspca.results$loadings,2,function(x) length(which(x!=0))/length(x))
# #Existing in how many PCs for each feature
# table(apply(rspca.results$loadings,1,function(x) length(which(x!=0))))
# 
# summary(rspca.results)

# temp=rspca.results$loadings
# temp[temp==0]=NA
# ComplexHeatmap::Heatmap(temp,cluster_columns = FALSE,cluster_rows =  FALSE)

sparsePCA loading Vis

for (reduction in c("ALL_SparsePCA","AreaShape_SparsePCA","RadialDistribution_SparsePCA")) {
  plot(plotPCsLoading(featureDataAllNormlized[["Reductions"]][[reduction]]$feature.loadings[,1:9])+ggtitle(reduction))
}

for (reduction in c("Intensity_SparsePCA","Neighbors_SparsePCA","Texture_SparsePCA","Granularity_SparsePCA")) {
  plot(plotPCsLoading(featureDataAllNormlized[["Reductions"]][[reduction]]$feature.loadings[,1:3])+ggtitle(reduction))
}

# plotPCsLoading(featureDataAllNormlized[["Reductions"]]$AreaShape_SparsePCA$feature.loadings[,1:9])
# plotPCsLoading(featureDataAllNormlized[["Reductions"]]$RadialDistribution_SparsePCA$feature.loadings[,1:9])
# plotPCsLoading(featureDataAllNormlized[["Reductions"]]$Intensity_SparsePCA$feature.loadings[,1:4])
# plotPCsLoading(featureDataAllNormlized[["Reductions"]]$Neighbors_SparsePCA$feature.loadings[,1:4])
# plotPCsLoading(featureDataAllNormlized[["Reductions"]]$Texture_SparsePCA$feature.loadings[,1:4])
# plotPCsLoading(featureDataAllNormlized[["Reductions"]]$Granularity_SparsePCA$feature.loadings[,1:4])

Differential detection

Diff By cell type in all samples

testResult=testFeatureDiff(featureDataAllNormlized,dataName="AreaShape_PCA",groupColumn="CellType")
knitr::kable(head(testResult))

	pValue	mesangial	PEC	podocyte	pAdj
PC1	0.0000002	-1.0709680	1.3836765	-0.2211795	0.0000079
PC2	0.0000000	-1.7483061	1.2918140	0.7335462	0.0000000
PC3	0.3333083	0.1845570	-0.2341403	0.0332326	0.4812937
PC4	0.0015975	0.0359318	-0.3347513	0.3337718	0.0151033
PC5	0.1260320	-0.0398208	0.1939320	-0.1695555	0.2912740
PC6	0.1461189	0.0157159	0.1556479	-0.1960573	0.3233268

plotFeatureByGroup(featureDataAllNormlized,dataName="AreaShape_PCA",featureNames=c("PC1","PC2","PC4","PC5"),groupColumn="CellType")

Diff By Sample in one cell type

temp=DataSelection(featureDataAllNormlized,CellType="mesangial")

[1] “mesangial were selected as CellType” [1] “Number of Sample/CellType after selection:”

    mesangial

AF-10 93 AF-7 81 AF-8 52 AF-9 88

testResult=testFeatureDiff(temp,dataName="AreaShape_PCA",groupColumn="Sample")
knitr::kable(head(testResult))

	pValue	AF-10	AF-7	AF-8	AF-9	pAdj
PC1	0.0232520	-1.0038110	-1.5160756	0.5604785	-1.6962761	0.2969516
PC2	0.0140227	-1.2655242	-2.4173712	-1.5701714	-1.7479360	0.2916714
PC3	0.7268383	0.0880016	0.2199501	0.4413957	0.1022524	0.8688641
PC4	0.3827271	-0.1403118	-0.1022275	0.2612373	0.2162236	0.7196790
PC5	0.2570135	0.0164711	0.1119739	0.0826852	-0.3114212	0.6591459
PC6	0.0172450	-0.3151867	0.0401784	0.4218819	0.1028959	0.2934276

plotFeatureByGroup(temp,dataName="AreaShape_PCA",featureNames=c("PC2","PC5"),groupColumn="Sample")

#extract cells with largest PC2 and smallest PC2
cellEmbeddingsTable=temp[["Reductions"]][["AreaShape_PCA"]]$cell.embeddings[row.names(temp[["SampleData"]]),]

temp1=cellEmbeddingsTable[head(order(cellEmbeddingsTable[,"PC2"])),][,1:3]
temp2=cellEmbeddingsTable[tail(order(cellEmbeddingsTable[,"PC2"])),][,1:3]

Diff By Group in each cell type

featureDataAllNormlized[["SampleData"]]$Group=ifelse(featureDataAllNormlized[["SampleData"]]$Sample %in% c("AF-7","AF-8"),"Sample78","Sample910")

allCellTypes=unique(featureDataAllNormlized[["SampleData"]]$CellType)
testResultList=list()
for (cellType in allCellTypes) {
  for (dataName in c("AreaShape_PCA","Intensity_PCA","Neighbors_PCA","Texture_PCA","Granularity_PCA")) {
    temp=DataSelection(featureDataAllNormlized,CellType=cellType)
    testResult=testFeatureDiff(temp,dataName=dataName,groupColumn="Group")
    #knitr::kable(head(testResult))
    testResultList[[cellType]][[dataName]]=testResult
  }
}

[1] “PEC were selected as CellType” [1] “Number of Sample/CellType after selection:”

PEC

AF-10 85 AF-7 68 AF-8 50 AF-9 80 [1] “PEC were selected as CellType” [1] “Number of Sample/CellType after selection:”

PEC

AF-10 85 AF-7 68 AF-8 50 AF-9 80 [1] “PEC were selected as CellType” [1] “Number of Sample/CellType after selection:”

PEC

AF-10 85 AF-7 68 AF-8 50 AF-9 80 [1] “PEC were selected as CellType” [1] “Number of Sample/CellType after selection:”

PEC

AF-10 85 AF-7 68 AF-8 50 AF-9 80 [1] “PEC were selected as CellType” [1] “Number of Sample/CellType after selection:”

PEC

AF-10 85 AF-7 68 AF-8 50 AF-9 80 [1] “mesangial were selected as CellType” [1] “Number of Sample/CellType after selection:”

    mesangial

AF-10 93 AF-7 81 AF-8 52 AF-9 88 [1] “mesangial were selected as CellType” [1] “Number of Sample/CellType after selection:”

    mesangial

AF-10 93 AF-7 81 AF-8 52 AF-9 88 [1] “mesangial were selected as CellType” [1] “Number of Sample/CellType after selection:”

    mesangial

AF-10 93 AF-7 81 AF-8 52 AF-9 88 [1] “mesangial were selected as CellType” [1] “Number of Sample/CellType after selection:”

    mesangial

AF-10 93 AF-7 81 AF-8 52 AF-9 88 [1] “mesangial were selected as CellType” [1] “Number of Sample/CellType after selection:”

    mesangial

AF-10 93 AF-7 81 AF-8 52 AF-9 88 [1] “podocyte were selected as CellType” [1] “Number of Sample/CellType after selection:”

    podocyte

AF-10 84 AF-7 59 AF-8 31 AF-9 76 [1] “podocyte were selected as CellType” [1] “Number of Sample/CellType after selection:”

    podocyte

AF-10 84 AF-7 59 AF-8 31 AF-9 76 [1] “podocyte were selected as CellType” [1] “Number of Sample/CellType after selection:”

    podocyte

AF-10 84 AF-7 59 AF-8 31 AF-9 76 [1] “podocyte were selected as CellType” [1] “Number of Sample/CellType after selection:”

    podocyte

AF-10 84 AF-7 59 AF-8 31 AF-9 76 [1] “podocyte were selected as CellType” [1] “Number of Sample/CellType after selection:”

    podocyte

AF-10 84 AF-7 59 AF-8 31 AF-9 76

for (cellType in allCellTypes) {
  temp=DataSelection(featureDataAllNormlized,CellType=cellType)
  for (dataName in c("AreaShape_PCA","Intensity_PCA","Neighbors_PCA","Texture_PCA","Granularity_PCA")) {
    resultTable=head(testResultList[[cellType]][[dataName]])
    print(knitr::kable(resultTable,caption = paste0(cellType,": ",dataName)))
    if (any(resultTable[,1]<=0.05)) {
      p=plotFeatureByGroup(featureDataAllNormlized,dataName=dataName,featureNames=row.names(resultTable)[which(resultTable[,1]<=0.05)],groupColumn="Group")
      plot(p)
    }
  }
}

[1] “PEC were selected as CellType” [1] “Number of Sample/CellType after selection:”

PEC

AF-10 85 AF-7 68 AF-8 50 AF-9 80

PEC: AreaShape_PCA
	pValue	Sample78	Sample910	pAdj
PC1	0.1647764	1.9417594	0.9845627	0.9019341
PC2	0.0245740	2.0304581	0.7635715	0.8518979
PC3	0.3595083	0.0405176	-0.4305623	0.9740970
PC4	0.8446652	-0.3158944	-0.3482368	0.9847101
PC5	0.6300036	0.2955662	0.1212481	0.9811349
PC6	0.2848396	0.3101746	0.0451378	0.9740970

PEC: Intensity_PCA
	pValue	Sample78	Sample910	pAdj
PC1	0.6638668	-1.5211509	-1.7737663	0.9198485
PC2	0.1403125	-0.2361451	0.1111346	0.4810309
PC3	0.9354232	0.2082585	0.2461960	0.9354232
PC4	0.2969512	0.0683846	-0.0375718	0.5539626
PC5	0.2628852	-0.0487319	0.0482139	0.5539626
PC6	0.8585253	0.0290617	0.0430621	0.9198485

PEC: Neighbors_PCA
	pValue	Sample78	Sample910	pAdj
PC1	0.8956875	0.0704752	0.0879472	0.8956875
PC2	0.6030465	0.0339633	-0.0069593	0.8956875
PC3	0.4489284	-0.0899584	0.0100728	0.8956875

PEC: Texture_PCA
	pValue	Sample78	Sample910	pAdj
PC1	0.7750374	-2.8989028	-2.8591582	0.9776805
PC2	0.7291988	0.0077225	0.1316601	0.9776805
PC3	0.6713699	0.5252441	0.4435018	0.9776805
PC4	0.4595890	-0.3812918	-0.1950243	0.9776805
PC5	0.7192586	0.1146122	0.1268900	0.9776805
PC6	0.3250823	0.0586697	0.2163677	0.9776805

PEC: Granularity_PCA
	pValue	Sample78	Sample910	pAdj
PC1	0.2073675	0.2121116	0.3329175	0.6221024
PC2	0.8504178	-0.2461034	-0.4695262	0.8504178
PC3	0.6264301	-0.0043476	-0.0034621	0.8504178

[1] “mesangial were selected as CellType” [1] “Number of Sample/CellType after selection:”

    mesangial

AF-10 93 AF-7 81 AF-8 52 AF-9 88

mesangial: AreaShape_PCA
	pValue	Sample78	Sample910	pAdj
PC1	0.2174119	-0.7041898	-1.3404791	0.6824032
PC2	0.0446062	-2.0861352	-1.5000670	0.4079057
PC3	0.3620851	0.3065303	0.0949302	0.7515257
PC4	0.8125580	0.0398790	0.0330314	0.9707279
PC5	0.1358766	0.1005227	-0.1429462	0.5887988
PC6	0.0264400	0.1894158	-0.1119200	0.4079057

mesangial: Intensity_PCA
	pValue	Sample78	Sample910	pAdj
PC1	0.2417833	1.5986653	1.9108835	0.4533438
PC2	0.9243333	0.4138362	0.1682172	0.9243333
PC3	0.0033304	-0.1459608	0.2278047	0.0258003
PC4	0.1752702	-0.0128275	-0.1084464	0.3755791
PC5	0.0645175	-0.1354599	-0.0219442	0.1612938
PC6	0.3843626	0.0779116	0.0418579	0.5765439

mesangial: Neighbors_PCA
	pValue	Sample78	Sample910	pAdj
PC1	0.3255561	0.3059934	0.1088290	0.7281928
PC2	0.9023825	0.0231119	0.0257884	0.9023825
PC3	0.4854619	-0.0132588	0.0384762	0.7281928

mesangial: Texture_PCA
	pValue	Sample78	Sample910	pAdj
PC1	0.5985684	3.3181394	3.8201610	0.8412312
PC2	0.3061384	-0.6571568	-0.4364943	0.7783876
PC3	0.0291133	-0.6602700	-0.2857536	0.3784731
PC4	0.0015472	0.3086615	-0.0769440	0.0804545
PC5	0.9073655	-0.0271908	-0.0425272	0.9488461
PC6	0.0442070	-0.1738700	0.1084028	0.4495595

mesangial: Granularity_PCA
	pValue	Sample78	Sample910	pAdj
PC1	0.8931529	-0.2902456	-0.1637421	0.8931529
PC2	0.8604179	0.1432073	0.2101496	0.8931529
PC3	0.7503548	-0.0084826	-0.0049416	0.8931529

[1] “podocyte were selected as CellType” [1] “Number of Sample/CellType after selection:”

    podocyte

AF-10 84 AF-7 59 AF-8 31 AF-9 76

podocyte: AreaShape_PCA
	pValue	Sample78	Sample910	pAdj
PC1	0.0536558	0.7163934	-0.7485642	0.6846849
PC2	0.0454283	1.2657749	0.4341676	0.6846849
PC3	0.6227424	0.1249957	-0.0183841	0.9675981
PC4	0.0550258	-0.0586012	0.5544816	0.6846849
PC5	0.8740448	-0.0615449	-0.2303114	0.9825623
PC6	0.5909086	-0.3399449	-0.1151205	0.9675981

podocyte: Intensity_PCA
	pValue	Sample78	Sample910	pAdj
PC1	0.7790145	-0.4298479	-0.2977428	0.9705501
PC2	0.7374240	-0.3163762	-0.2967859	0.9705501
PC3	0.8411434	-0.3562226	-0.3434792	0.9705501
PC4	0.7401744	0.0400361	0.0991347	0.9705501
PC5	0.0568977	-0.0335524	0.1425178	0.4267328
PC6	0.6858403	-0.0575915	-0.1455612	0.9705501

podocyte: Neighbors_PCA
	pValue	Sample78	Sample910	pAdj
PC1	0.0089300	-0.6891165	-0.1325129	0.0267901
PC2	0.6632118	-0.0222363	-0.0537481	0.6898630
PC3	0.6898630	0.0288268	0.0072368	0.6898630

podocyte: Texture_PCA
	pValue	Sample78	Sample910	pAdj
PC1	0.8397188	-1.3203371	-1.2506231	0.9492473
PC2	0.0189320	0.2608087	0.7518710	0.6359509
PC3	0.1995764	0.1966523	-0.0832375	0.6505838
PC4	0.7874134	0.1968214	0.2020775	0.9455231
PC5	0.9709299	-0.1118807	-0.0817377	0.9767421
PC6	0.1526167	-0.2635446	-0.0962554	0.6359509

podocyte: Granularity_PCA
	pValue	Sample78	Sample910	pAdj
PC1	0.1293987	-0.1597717	0.0166181	0.1940980
PC2	0.0031105	0.0246511	0.2950611	0.0093314
PC3	0.2034788	0.0260892	0.0047428	0.2034788

# temp=DataSelection(featureDataAllNormlized,CellType="mesangial")
# plotFeatureByGroup(temp,dataName="AreaShape_PCA",featureNames=c("PC2","PC6"),groupColumn="Group")
# plotFeatureByGroup(temp,dataName="Intensity_PCA",featureNames=c("PC3","PC5"),groupColumn="Group")
# plotFeatureByGroup(temp,dataName="Texture_PCA",featureNames=c("PC3","PC6"),groupColumn="Group")
# 
# temp=DataSelection(featureDataAllNormlized,CellType="PEC")
# plotFeatureByGroup(temp,dataName="AreaShape_PCA",featureNames=c("PC2"),groupColumn="Group")
# 
# temp=DataSelection(featureDataAllNormlized,CellType="podocyte")
# plotFeatureByGroup(temp,dataName="AreaShape_PCA",featureNames=c("PC1","PC2","PC4"),groupColumn="Group")
# plotFeatureByGroup(temp,dataName="Intensity_PCA",featureNames=c("PC5"),groupColumn="Group")
# plotFeatureByGroup(temp,dataName="Neighbors_PCA",featureNames=c("PC1"),groupColumn="Group")
# plotFeatureByGroup(temp,dataName="Texture_PCA",featureNames=c("PC2"),groupColumn="Group")
# plotFeatureByGroup(temp,dataName="Granularity_PCA",featureNames=c("PC2"),groupColumn="Group")

#PC2 is more related
#extract cells with largest PC2 and smallest PC2
cellEmbeddingsTable=featureDataAllNormlized[["Reductions"]][["AreaShape_PCA"]]$cell.embeddings
temp1=cellEmbeddingsTable[head(order(cellEmbeddingsTable[,"PC2"])),][,1:3]
temp2=cellEmbeddingsTable[tail(order(cellEmbeddingsTable[,"PC2"])),][,1:3]

#dput(row.names(temp1))
#dput(row.names(temp2))

Save results

saveRDS(featureDataAllNormlized,file.path(dataDir,"featureDataAllNormlized.rds"))